US7231093B2  Image code estimation  Google Patents
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 US7231093B2 US7231093B2 US10633158 US63315803A US7231093B2 US 7231093 B2 US7231093 B2 US 7231093B2 US 10633158 US10633158 US 10633158 US 63315803 A US63315803 A US 63315803A US 7231093 B2 US7231093 B2 US 7231093B2
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 H—ELECTRICITY
 H04—ELECTRIC COMMUNICATION TECHNIQUE
 H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
 H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
 H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
 H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
 H04N19/18—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a set of transform coefficients

 H—ELECTRICITY
 H04—ELECTRIC COMMUNICATION TECHNIQUE
 H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
 H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
 H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
 H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
 H04N19/124—Quantisation

 H—ELECTRICITY
 H04—ELECTRIC COMMUNICATION TECHNIQUE
 H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
 H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
 H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
 H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
 H04N19/136—Incoming video signal characteristics or properties
 H04N19/14—Coding unit complexity, e.g. amount of activity or edge presence estimation

 H—ELECTRICITY
 H04—ELECTRIC COMMUNICATION TECHNIQUE
 H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
 H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
 H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
 H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
 H04N19/146—Data rate or code amount at the encoder output
 H04N19/149—Data rate or code amount at the encoder output by estimating the code amount by means of a model, e.g. mathematical model or statistical model

 H—ELECTRICITY
 H04—ELECTRIC COMMUNICATION TECHNIQUE
 H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
 H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
 H04N19/60—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding
Abstract
Description
The following applications disclose related subject matter: application. Ser. No. 10/917,980, filed Aug. 13, 2004 and Ser. No. 11/218,407, filed Sep. 1, 2005. These referenced applications have a common assignee with the present application.
The present invention relates to digital image processing, and more particularly to compressive encoding of images.
In many applications of digital image processing, such as JPEG, MPEG or DV, image data is compressed by successive operations of DCT (Discrete Cosine Transform), quantization, and Huffman (variable length) encoding. DCT is a preprocessing operation for image compression which converts blocks of spatial domain information of the image into blocks of frequency domain information, typically for 8×8 blocks of pixels. Generally, the transformed image has a tendency to strong correlations within a neighborhood; DCT processing concentrates a majority of the information of the image into the low frequencies. Quantization is an effective compression method which divides the DCT coefficients by an integer quantization level so as to reduce the precision (number of significant bits) of the DCT coefficients. The quantized DCT coefficients of a block are scanned from low frequency to high frequency and converted into a sequence of pairs of ‘run’ and ‘level’ parameters (run length encoding). Using a Huffman code table defined by the statistics of an image, the sequence of runlevel pairs are finally converted to a sequence of Huffman (variable length) codewords. The Huffman tables for differing sets of variables of JPEG, MPEG, or DV are fixed in the specifications of these standards.
In these image compression methods, the size of the Huffman code generated is strongly dependent on the quantization level. Therefore, it is necessary to apply a suitable quantization level to adjust an output code size to a target code size. One useful quantization level approach has a feedback loop comparing a target code size and a code size actually generated by the encoder circuit. This approach enables estimation and response to a current code size accurately because actual output code size is used as a comparison to the target code size. However, there is a delay corresponding to the speed of the encoding algorithm to output the actual code. As a result, there is a delay for convergence to the target code size.
The present invention provides estimation of the code size for digital image compression by simplifying the feedback flow for the quantization process for DCT coefficients and Huffman encoding.
This has advantages including more efficient quantization without time delay of actual code generation.
The drawings are heuristic for clarity.
1. Overview
Preferred embodiment image processing methods include low complexity estimates of the code size for Huffman (variable length code) encoding a block of quantized DCT coefficients; this provides quantization level feedback information for selection of a quantization level(s). The method uses a histogram of the nonzero DCT coefficient magnitudes of (an area of) a block together with normalized codesize functions (a function for representative “level”s and depending upon an average “run”) from the Huffman table. The average “run” in the code is estimated from the number of zero and nonzero quantized coefficients in (the area of) the block.
Preferred embodiment digital image systems (such as cameras) include preferred embodiment image processing methods.
2. First Preferred Embodiment
Many of the specifications in standards such as JPEG, MPEG, take the DCT coefficient quantization level to depend upon the DCT frequency; that is, a nonflat quantization matrix. Such frequencydependent quantization levels use the fact that the sensitivity of the human visual system to high spatial frequency components in an image is less than that to low spatial frequency components. In this case, partitioning the DCT coefficient block into several areas which correspond to spatial frequency ranges increases the accuracy of the estimation of generated code size.
H(i,j): the number of coefficients in bin 2^{i}˜2^{i+1}−1(i=0, 1, 2 . . . , N−1) in area j
The preferred embodiment methods use such a histogram with bins of the ranges 2^{n}˜2^{n+1}−1 for a fast approximation of the quantization process. In particular, for the case of the quantization levels defined in a specification under consideration confined to 2^{M }(M=0, 1, 2 . . . ), the quantization level changes are achieved by shifting values of each bin of the histogram. That is, with quantization level 2^{M }the number of quantized coefficients, Hq(i,j), within the bin 2^{i}˜2^{i+1}−1 (i=0,1,2 . . . , N−1) in area j is given by:
Even if the quantization levels defined in the specification under consideration are arbitrary integers, the histogram after the quantization Hq(i,j) can be represented approximately by scaling a graph of the distribution given by the original histogram H(i,j).
The variablelength code size of the block after quantization is estimated from the given Hq(i,j) by simplifying the Huffman table. Inputs for the Huffman table are the parameters “run” and “level”, so consider an expressions for these parameters using Hq(i,j). Note that the sign of a coefficient typically is the last bit of the codeword, so coefficient differing by sign have the same magnitude and same codeword size. The parameter “run” is the number of zero coefficients between significant coefficients when scanning the DCT block from the low frequency (upper lefthand portion in
so estimate “run” by r′(j) as follows.
As to the parameter “level”, it already is implicit in the histogram bins for the magnitudes of the DCT coefficients in area j. Therefore, if an average value is utilized as a representative for the bin of the range 2^{i}˜2^{i+1}−1, the parameter “level” can be estimated for coefficients in the bin by l′(i):
This can also be simplified to the maximum value 2^{i+1}−1 in the case that the specification under consideration has a restrictive upper limit on the generated code size. That is,
l′(i)≡2^{i+1}−1
In any case, the representative value l′(i) is not dependent on the areaj. Now, r′(j) and l′(i) provide input parameters for the Huffman table. If the code size of the entry “run”=r and “level” =l in the Huffman table under consideration is denoted as T(r,l), then the code size c(j) for the coefficients in area j of the DCT block can be estimated as follows.
In this equation, T(r, l) is only applied to the representative value, l′(i), for each bin i. Therefore, for this estimation, an original Huffman table can be reduced to the table restricted to the representative “level”s. However, this calculation contains an undesirable division operation from the definition of r′(j). The division can be avoided by using a normalized function T′(x,i) for each bin i where x is a normalized variable of the number of nonzero coefficients applicable to all areas. As shown in the expression for r′(j), it is determined by the fixed number of coefficients in area j, A(j), and the variable number of nonzero coefficients in area j, ΣHq(i,j). By normalizing (scaling) the number A(j) to a convenient integer A(=α(j)A(j) and the number ΣHq(i,j) to ΣHq(i)(=α(j)ΣHq(i, j)), a function of the Huffman table T(r′(i), l′(i)) can be redefined independent of area j. Thus, code size c(j) can be represented as follows:
That is, x is the number of nonzero coefficients in A(j) normalized to a total of A coefficients; hence, the average “run” in A(j) is r′(j)=(A−x)/x and so the definition T′(x,i)=T((A−x)/x,l′(i)) essentially precomputes the division in r′(j).
As a result, a total code size, S, for the DCT block can be approximated as
Thus, a total code size for each block of DCT coefficients can be calculated by the summation of the code sizes of the coefficients in the areas in the DCT block which, in turn, is estimated by the histogram for each area by simplified operation for the quantization level and the Huffman coding (T′(x,i)) without performing an actual encoding.
As illustrated in
3. Experimental Results
4. Modifications
The preferred embodiment code size estimation methods can be varied in various ways while preserving the feature of estimating the code size from a histogram of coefficient magnitudes together with the normalized code size functions from the Huffman (variable length code) table.
For example, the DCT coefficient block size could be increased (e.g., 16×16) or decreased, the number of areas in a block could be varied from 1 to any convenient number, normalization size A for the normalized code size functions T′(x,i) can all be varied, and so forth. The same code size estimation as a function of quantization level could be applied to coefficients of a wavelet transform in place of the DCT.
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US20110310961A1 (en) *  20100617  20111222  Microsoft Corporation  Video rate control based on transformcoefficients histogram 
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Citations (5)
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US5901250A (en) *  19940713  19990504  Matsushita Electric Industrial Co., Ltd.  Digital coding apparatus and digital coding/decoding apparatus 
US6614938B2 (en) *  19941110  20030902  Kabushiki Kaisha Toshiba  Image compression with orthogonal transform of blocks within a bounded shape 
US6658157B1 (en) *  19990629  20031202  Sony Corporation  Method and apparatus for converting image information 
US6711295B2 (en) *  19981006  20040323  Canon Kabushiki Kaisha  Encoding apparatus and method, and storage medium 
US6909811B1 (en) *  19980306  20050621  Canon Kabushiki Kaisha  Image processing apparatus and method and storage medium storing steps realizing such method 
Patent Citations (5)
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US5901250A (en) *  19940713  19990504  Matsushita Electric Industrial Co., Ltd.  Digital coding apparatus and digital coding/decoding apparatus 
US6614938B2 (en) *  19941110  20030902  Kabushiki Kaisha Toshiba  Image compression with orthogonal transform of blocks within a bounded shape 
US6909811B1 (en) *  19980306  20050621  Canon Kabushiki Kaisha  Image processing apparatus and method and storage medium storing steps realizing such method 
US6711295B2 (en) *  19981006  20040323  Canon Kabushiki Kaisha  Encoding apparatus and method, and storage medium 
US6658157B1 (en) *  19990629  20031202  Sony Corporation  Method and apparatus for converting image information 
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